Local-field and excitonic effects in the optical response of alpha-alumina

This paper introduces key ingredients of the dielectric response of a-alumina that go beyond an independent-particle (IP) treatment of the valence-electron excitations. The optical-response functions were calculated from first-principles both at the Bethe-Salpeter and the random-phase approximation (RPA) levels. Excitonic effects obtained within the Bethe-Salpeter framework were found essential for reproducing the low-energy part of the experimental spectra (below 15 eV) and the bound exciton in particular. For higher energies, local-field effects introduced through the RPA modified considerably the IP results and provided a satisfactory account of the reflectivity spectra and of the position and shape of the dominant bulk plasmon resonance in the electron energy-loss spectra.

Recoupled separated-local-field experiments and applications to study intermediate-regime molecular motions

A specific separated-local-field NMR experiment, dubbed Dipolar-Chemical-Shift Correlation (DIPSHIFT) is frequently used to study molecular motions by probing reorientations through the changes in XH dipolar coupling and T-2. In systems where the coupling is weak or the reorientation angle is small, a recoupled variant of the DIPSHIFT experiment is applied, where the effective dipolar coupling is amplified by a REDOR-like pi-pulse train. However, a previously described constant-time variant of this experiment is not sensitive to the motion-induced T-2 effect, which precludes the observation of motions over a large range of rates ranging from hundreds of Hz to around a MHz. We present a DIPSHIFT implementation which amplifies the dipolar couplings and is still sensitive to T-2 effects. Spin dynamics simulations, analytical calculations and experiments demonstrate the sensitivity of the technique to molecular motions, and suggest the best experimental conditions to avoid imperfections. Furthermore, an in-depth theoretical analysis of the interplay of REDOR-like recoupling and proton decoupling based on Average-Hamiltonian Theory was performed, which allowed explaining the origin of many artifacts found in literature data. (C) 2012 Elsevier Inc. All rights reserved.

Electrogram analysis and pacing are complimentary for recognition of abnormal conduction and far-field potentials during substrate mapping of infarct-related ventricular tachycardia.

BACKGROUND Mapping to identify scar-related ventricular tachycardia re-entry circuits during sinus rhythm focuses on sites with abnormal electrograms or pace-mapping findings of QRS morphology and long stimulus to QRS intervals. We hypothesized that (1) these methods do not necessarily identify the same sites and (2) some electrograms are far-field potentials that can be recognized by pacing. METHODS AND RESULTS From 12 patients with coronary disease and recurrent ventricular tachycardia undergoing catheter ablation, we retrospectively analyzed electrograms and pacing at 546 separate low bipolar voltage (<1.5 mV) sites. Electrograms were characterized as showing evidence of slow conduction if late potentials (56%) or fractionated potentials (76%) were present. Neither was present at (13%) sites. Pacing from the ablation catheter captured 70% of all electrograms. Higher bipolar voltage and fractionation were independent predictors for pace capture. There was a linear correlation between the stimulus to QRS duration during pacing and the lateness of a capturing electrogram (P<0.001), but electrogram and pacing markers of slow conduction were discordant at 40% of sites. Sites with far-field potentials, defined as those that remained visible and not captured by pacing stimuli, were identified at 48% of all pacing sites, especially in areas of low bipolar voltage and late potentials. Initial radiofrequency energy application rendered 74% of targeted sites electrically unexcitable. CONCLUSIONS Far-field potentials are common in scar areas. Combining analysis of electrogram characteristics and assessment of pace capture may refine identification of substrate targets for radiofrequency ablation.

Medida de uma codificação de estímulos sonoros por potenciais pós-sinápticos e potenciais de campo local usando teoria da informação

Coding process is a fundamental aspect of cerebral functioning. The sensory stimuli transformation in neurophysiological responses has been a research theme in several areas of Neuroscience. One of the most used ways to measure a neural code e ciency is by the use of Information Theory measures, such as mutual information. Using these tools, recent studies show that in the auditory cortex both local eld potentials (LFPs) and action potential spiking times code information about sound stimuli. However, there are no studies applying Information Theory tools to investigate the e ciency of codes that use postsynaptics potentials (PSPs), alone and associated with LFP analysis. These signals are related in the sense that LFPs are partly created by joint action of several PSPs. The present dissertation reports information measures between PSP and LFP responses obtained in the primary auditory cortex of anaesthetized rats and auditory stimuli of distinct frequencies. Our results show that PSP responses hold information about sound stimuli in comparable levels and even greater than LFP responses. We have also found that PSPs and LFPs code sound information independently, since the joint analysis of these signals did neither show synergy nor redundancy.

Denoising neural data with state-space smoothing: Method and application

Neural data are inevitably contaminated by noise. When such noisy data are subjected to statistical analysis, misleading conclusions can be reached. Here we attempt to address this problem by applying a state-space smoothing method, based on the combined use of the Kalman filter theory and the Expectation–Maximization algorithm, to denoise two datasets of local field potentials recorded from monkeys performing a visuomotor task. For the first dataset, it was found that the analysis of the high gamma band (60–90 Hz) neural activity in the prefrontal cortex is highly susceptible to the effect of noise, and denoising leads to markedly improved results that were physiologically interpretable. For the second dataset, Granger causality between primary motor and primary somatosensory cortices was not consistent across two monkeys and the effect of noise was suspected. After denoising, the discrepancy between the two subjects was significantly reduced.

Analyzing information flow in brain networks with nonparametric Granger causality

Multielectrode neurophysiological recording and high-resolution neuroimaging generate multivariate data that are the basis for understanding the patterns of neural interactions. How to extract directions of information flow in brain networks from these data remains a key challenge. Research over the last few years has identified Granger causality as a statistically principled technique to furnish this capability. The estimation of Granger causality currently requires autoregressive modeling of neural data. Here, we propose a nonparametric approach based on widely used Fourier and wavelet transforms to estimate both pairwise and conditional measures of Granger causality, eliminating the need of explicit autoregressive data modeling. We demonstrate the effectiveness of this approach by applying it to synthetic data generated by network models with known connectivity and to local field potentials recorded from monkeys performing a sensorimotor task.

Fast Robust Pattern Classification Algorithms for Real Time Neuro-Motor Prosthetic Applications

In this paper, we give a brief review of pattern classification algorithms based on discriminant analysis. We then apply these algorithms to classify movement direction based on multivariate local field potentials recorded from a microelectrode array in the primary motor cortex of a monkey performing a reaching task. We obtain prediction accuracies between 55% and 90% using different methods which are significantly above the chance level of 12.5%.

A study on fear memory retrieval and REM sleep in maternal separation and isolation stressed rats

As rapid brain development occurs during the neonatal period, environmental manipulation during this period may have a significant impact on sleep and memory functions. Moreover, rapid eye movement (REM) sleep plays an important role in integrating new information with the previously stored emotional experience. Hence, the impact of early maternal separation and isolation stress (MS) during the stress hyporesponsive period (SHRP) on fear memory retention and sleep in rats were studied. The neonatal rats were subjected to maternal separation and isolation stress during postnatal days 5-7 (6 h daily/3 d). Polysomnographic recordings and differential fear conditioning was carried out in two different sets of rats aged 2 months. The neuronal replay during REM sleep was analyzed using different parameters. MS rats showed increased time in REM stage and total sleep period also increased. MS rats showed fear generalization with increased fear memory retention than normal control (NC). The detailed analysis of the local field potentials across different time periods of REM sleep showed increased theta oscillations in the hippocampus, amygdala and cortical circuits. Our findings suggest that stress during SHRP has sensitized the hippocampus amygdala cortical loops which could be due to increased release of corticosterone that generally occurs during REM sleep. These rats when subjected to fear conditioning exhibit increased fear memory and increased, fear generalization. The development of helplessness, anxiety and sleep changes in human patients, thus, could be related to the reduced thermal, tactile and social stimulation during SHRP on brain plasticity and fear memory functions. (C) 2014 Elsevier B.V. All rights reserved.

HCN channels enhance spike phase coherence and regulate the phase of spikes and LFPs in the theta-frequency range

What are the implications for the existence of subthreshold ion channels, their localization profiles, and plasticity on local field potentials (LFPs)? Here, we assessed the role of hyperpolarization-activated cyclic-nucleotide-gated (HCN) channels in altering hippocampal theta-frequency LFPs and the associated spike phase. We presented spatiotemporally randomized, balanced theta-modulated excitatory and inhibitory inputs to somatically aligned, morphologically realistic pyramidal neuron models spread across a cylindrical neuropil. We computed LFPs from seven electrode sites and found that the insertion of an experimentally constrained HCN-conductance gradient into these neurons introduced a location- dependent lead in the LFP phase without significantly altering its amplitude. Further, neurons fired action potentials at a specific theta phase of the LFP, and the insertion of HCN channels introduced large lags in this spike phase and a striking enhancement in neuronal spike-phase coherence. Importantly, graded changes in either HCN conductance or its half-maximal activation voltage resulted in graded changes in LFP and spike phases. Our conclusions on the impact of HCN channels on LFPs and spike phase were invariant to changes in neuropil size, to morphological heterogeneity, to excitatory or inhibitory synaptic scaling, and to shifts in the onset phase of inhibitory inputs. Finally, we selectively abolished the inductive lead in the impedance phase introduced by HCN channels without altering neuronal excitability and found that this inductive phase lead contributed significantly to changes in LFP and spike phase. Our results uncover specific roles for HCN channels and their plasticity in phase-coding schemas and in the formation and dynamic reconfiguration of neuronal cell assemblies.

Rhythm generation in monkey motor cortex explored using pyramidal tract stimulation.

We investigated whether stimulation of the pyramidal tract (PT) could reset the phase of 15-30 Hz beta oscillations observed in the macaque motor cortex. We recorded local field potentials (LFPs) and multiple single-unit activity from two conscious macaque monkeys performing a precision grip task. EMG activity was also recorded from the second animal. Single PT stimuli were delivered during the hold period of the task, when oscillations in the LFP were most prominent. Stimulus-triggered averaging of the LFP showed a phase-locked oscillatory response to PT stimulation. Frequency domain analysis revealed two components within the response: a 15-30 Hz component, which represented resetting of on-going beta rhythms, and a lower frequency 10 Hz response. Only the higher frequency could be observed in the EMG activity, at stronger stimulus intensities than were required for resetting the cortical rhythm. Stimulation of the PT during movement elicited a greatly reduced oscillatory response. Analysis of single-unit discharge confirmed that PT stimulation was capable of resetting periodic activity in motor cortex. The firing patterns of pyramidal tract neurones (PTNs) and unidentified neurones exhibited successive cycles of suppression and facilitation, time locked to the stimulus. We conclude that PTN activity directly influences the generation of the 15-30 Hz rhythm. These PTNs facilitate EMG activity in upper limb muscles, contributing to corticomuscular coherence at this same frequency. Since the earliest oscillatory effect observed following stimulation was a suppression of firing, we speculate that inhibitory feedback may be the key mechanism generating such oscillations in the motor cortex.

KInNeSS: A Modular Framework for Computational Neuroscience

Making use of very detailed neurophysiological, anatomical, and behavioral data to build biological-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalabiltiy, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multu-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions of ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further developement of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effecitively collaborate using a modern neural simulation platform.

Spikes, Synchrony, and Attentive Learning by Laminar Thalamocortical Circuits

This article develops the Synchronous Matching Adaptive Resonance Theory (SMART) neural model to explain how the brain may coordinate multiple levels of thalamocortical and corticocortical processing to rapidly learn, and stably remember, important information about a changing world. The model clarifies how bottom-up and top-down processes work together to realize this goal, notably how processes of learning, expectation, attention, resonance, and synchrony are coordinated. The model hereby clarifies, for the first time, how the following levels of brain organization coexist to realize cognitive processing properties that regulate fast learning and stable memory of brain representations: single cell properties, such as spiking dynamics, spike-timing-dependent plasticity (STDP), and acetylcholine modulation; detailed laminar thalamic and cortical circuit designs and their interactions; aggregate cell recordings, such as current-source densities and local field potentials; and single cell and large-scale inter-areal oscillations in the gamma and beta frequency domains. In particular, the model predicts how laminar circuits of multiple cortical areas interact with primary and higher-order specific thalamic nuclei and nonspecific thalamic nuclei to carry out attentive visual learning and information processing. The model simulates how synchronization of neuronal spiking occurs within and across brain regions, and triggers STDP. Matches between bottom-up adaptively filtered input patterns and learned top-down expectations cause gamma oscillations that support attention, resonance, and learning. Mismatches inhibit learning while causing beta oscillations during reset and hypothesis testing operations that are initiated in the deeper cortical layers. The generality of learned recognition codes is controlled by a vigilance process mediated by acetylcholine.

KInNeSS: A Modular Framework for Computational Neuroscience

Making use of very detailed neurophysiological, anatomical, and behavioral data to build biologically-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalability, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multi-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions or ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further development of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effectively collaborate using a modern neural simulation platform.